U.S. patent number 11,210,788 [Application Number 16/457,199] was granted by the patent office on 2021-12-28 for system and method for performing quality control.
This patent grant is currently assigned to James R. Glidewell Dental Ceramics, Inc.. The grantee listed for this patent is James R. Glidewell Dental Ceramics, Inc.. Invention is credited to Abhishek Babasaheb Ajri, Marco Antonio Jokada, David Christopher Leeson, Vaheh Golestanian Nemagrdi.
United States Patent |
11,210,788 |
Ajri , et al. |
December 28, 2021 |
System and method for performing quality control
Abstract
Disclosed are example embodiments of methods and systems for
identifying and quantifying manufacturing defects of a manufactured
dental prosthesis. Certain embodiments of the system for performing
quality control on manufactured dental prostheses includes: a
quality control module configured to determine whether the dental
prosthesis is a good or a defective product based at least on a
differences model generated by comparing a design model and a
scanned model of the manufactured dental prosthesis.
Inventors: |
Ajri; Abhishek Babasaheb (Lake
Forest, CA), Nemagrdi; Vaheh Golestanian (Orange, CA),
Jokada; Marco Antonio (Diamond Bar, CA), Leeson; David
Christopher (North Tustin, CA) |
Applicant: |
Name |
City |
State |
Country |
Type |
James R. Glidewell Dental Ceramics, Inc. |
Newport Beach |
CA |
US |
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Assignee: |
James R. Glidewell Dental Ceramics,
Inc. (Newport Beach, CA)
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Family
ID: |
1000006018517 |
Appl.
No.: |
16/457,199 |
Filed: |
June 28, 2019 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20190318479 A1 |
Oct 17, 2019 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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15928484 |
Mar 22, 2018 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F
17/18 (20130101); G06T 7/0014 (20130101); G06F
30/23 (20200101); G06T 2207/30052 (20130101); G06F
2119/18 (20200101) |
Current International
Class: |
G06T
7/00 (20170101); G06F 17/18 (20060101); G06F
30/23 (20200101) |
Field of
Search: |
;703/6 |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Pompa et al. ("Comparison of Conventional Methods and
Laser-Assisted Rapid Prototyping for Manufacturing Fixed Dental
Prostheses: An In Vitro Study", BioMed Research International,
2015, pp. 1-7) (Year: 2015). cited by examiner .
Gary L Henkel ("A Comparison of Fixed Prostheses Generated from
Conventional vs Digitally Scanned Dental Impressions", Private
Practice, Horsham, Pennsylvania, 2007, pp. 1-8) (Year: 2007). cited
by examiner .
Maria Averyanova, Quality Control of Dental Bridges and Removable
Prostheses Manufactured Using Phenix Systems Equipment, AEPR 12,
17th European Forum on Rapid Prototyping and Manufacturing Paris,
France, Jun. 12-14, 2012. cited by applicant.
|
Primary Examiner: Khan; Iftekhar A
Attorney, Agent or Firm: Fowler; Charles
Parent Case Text
CROSS-REFERENCE TO RELATED APPLICATIONS
This present application is a continuation-in-part of U.S. patent
application Ser. No. 15/928,484, filed Mar. 22, 2018, the
disclosures of which is incorporated herein by reference in its
entireties for all purposes.
Claims
What is claimed is:
1. A method for performing quality control on a manufactured dental
prosthesis, the method comprising: scanning a manufactured dental
prosthesis to generate a scanned model; and determining whether the
manufactured dental prosthesis is a good or a defective product
based at least on a differences model generated by comparing
spatial points of a design model and corresponding best-fitting
points of the scanned model to compile offsets between the spatial
points of the design model and the corresponding best-fitting
spatial points of the scanned model; wherein the design model
comprises a computer generated model based upon either: (i) a
design of the dental prosthesis based upon patient-specific data,
or (ii) a simulation of a manufacturing process for the dental
prosthesis.
2. The method of claim 1, wherein determining whether the
manufactured dental prosthesis is a good or defective product
further comprises performing a statistical analysis on the
differences model.
3. The method of claim 2, wherein determining whether the
manufactured dental prosthesis is a good or defective product
further comprises determining a standard deviation of offset values
of points of the differences model.
4. The method of claim 3, wherein the manufactured dental
prosthesis comprises an anterior crown and wherein the anterior
crown is determined to be a defective product if the standard
deviation is above 20 microns.
5. The method of claim 1, wherein the differences model generated
by comparing the design model and the scanned model further
comprises omitting from the differences model data points below a
margin line of the differences model.
6. The method of claim 5, wherein the margin line has a height
ranging between 10-20% of an overall height of the manufactured
dental prosthesis.
7. The method of claim 5, wherein omitting from the differences
model further comprises omitting data points of an occlusal surface
of the manufactured dental prosthesis.
8. The method of claim 7, wherein the manufactured dental
prosthesis is a bridge, and wherein omitting from the differences
model further comprises omitting data points of an occlusal surface
of each tooth of the bridge.
9. The method of claim 8, wherein the differences model generated
by comparing the design model and the scanned model further
comprises omitting data points of surfaces between any two teeth of
the bridge.
10. The method of claim 1, wherein the differences model generated
by comparing the design model and the scanned model further
comprises omitting from the differences model data points of sprue
locations, wherein a sprue location is where a sprue is placed to
secure the manufactured dental prosthesis within a milling
block.
11. The method of claim 1, further comprises determining that the
manufactured dental prosthesis is too small or too large based on a
distribution of differences that is negatively or positively biased
as compared to a normal distribution.
12. The method of claim 1, further comprises determining that the
manufactured dental prosthesis has a manufacturing defect when a
distribution of differences includes peaks at a left and right side
of a normal distribution curve.
13. The method of claim 1, further comprises: determining whether
the manufactured dental prosthesis is too small or too large based
on a distribution of differences that is negatively or positively
biased as compared to a normal distribution; determining whether
the manufactured dental prosthesis has a manufacturing defect when
a distribution of differences includes peaks at a left and right
side of a normal distribution curve; and shutting down one or more
crown manufacturing machineries based at least on whether the
manufactured dental prosthesis is too small, too large, or has a
step.
14. A method for performing quality control on a dental prosthesis,
the method comprising: generating a design model of a dental
prosthesis, the design model comprising a computer generated model
based upon either: (i) a design of the dental prosthesis based upon
patient-specific data, or (ii) a simulation of a manufacturing
process for the dental prosthesis; manufacturing the dental
prosthesis based on the generated design model; scanning the
manufactured dental prosthesis to create a 3D scan model;
generating a differences model by comparing the design model with
the 3D scan model and compiling offsets between spatial points of
the 3D scan model and corresponding spatial points of the design
model; and determining whether the manufactured dental prosthesis
is a good or a defective product based at least on an analysis of
the differences model.
15. The method of claim 14, wherein the simulated manufacturing
process comprises a simulated CNC milling or 3D printing
process.
16. The method of claim 1, wherein the simulated manufacturing
process comprises a simulated CNC milling or 3D printing process.
Description
TECHNICAL FIELD
The disclosure relates generally to the field of quality control,
specifically and not by way of limitation, some embodiments are
related to automatically performing quality control on manufactured
dental prostheses.
BACKGROUND
Recently, CAD/CAM dentistry (Computer-Aided Design and
Computer-Aided Manufacturing in dentistry) has provided a broad
range of dental restorations, including crowns, veneers, inlays and
onlays, fixed bridges, dental implant restorations, and orthodontic
appliances. In a typical CAD/CAM based dental procedure, a treating
dentist can prepare the tooth being restored either as a crown,
inlay, onlay, or veneer. The prepared tooth and its surroundings
are then scanned by a three-dimensional (3D) imaging camera and
uploaded to a computer for design. Alternatively, a dentist can
obtain an impression of the tooth to be restored and the impression
may be scanned directly, or formed into a model to be scanned, and
uploaded to a computer for design.
Dental prostheses are typically manufactured at specialized dental
laboratories that employ computer-aided design (CAD) and
computer-aided manufacturing (CAM) milling systems to produce
dental prostheses according to patient-specific specifications
provided by dentists. In a typical work flow, information about the
oral situation of a patient is received from a dentist, and the
dentist or dental laboratory designs the dental prosthesis. Where
the prosthesis is milled from a block of material, a material block
having a size, shape, color, and material-type properties suitable
for creating the prosthesis is selected.
After the milling process, the milled material blocks are cleaned.
Subsequent to the cleaning process, the milled material blocks are
manually transferred, inspected, and logged from the milling and
cleaning processes to a sintering tray in preparation for the
glazing process. A final inspection process may be performed after
the sintering process. Conventionally, the final inspection process
is done manually. In other words, each manufactured dental
prosthesis is visually inspected by a quality control (QC)
personnel. However, certain defects such as improper size (i.e.,
too small or too large) and milling defects such as steps are very
hard to visually detect. Even the most well-trained and seasoned QC
personnel will have a hard time detecting these types of defects.
Furthermore, in a situation in which a dental prosthesis is
manufactured without a physical dental model, there may be no
physical reference for fit testing. Even when there is a model
available (e.g., a stone or resin model that is poured or additive
manufactured), the model itself may also contain dimensional errors
that are often larger than is typical in manufacturing the
prosthesis. Additionally, even if the QC personnel can detect a
step or a fitting issue, it is virtually impossible for the QC
personnel to quantify the error. The failure to quantify the
defects make it very difficult for QC engineers to take corrective
actions. Accordingly, what is needed is a system and method for
performing quality control by identifying and quantifying
manufacturing defects of dental prostheses.
SUMMARY
Disclosed are example embodiments of methods and systems for
identifying and quantifying manufacturing defects of a manufactured
dental prosthesis. One of the methods for performing quality
control on a manufactured dental prosthesis comprises: determining
whether the manufactured dental prosthesis is a good or a defective
product based at least on a differences model generated by
comparing a design model and a scanned model of the manufactured
dental prosthesis. The differences model can comprise of points
with offset values determined by difference in distance between a
point in the design model and the corresponding best-fitting point
in the scanned model of the manufactured dental prosthesis. In a
perfect match, the offset value is zero.
The manufactured dental prosthesis can be determined to be a good
or defective product based at least on one or more statistical
analysis of the differences model. The manufactured dental
prosthesis can be also determined to be a good or defective product
based at least on the standard deviation of offset values of the
differences model. The manufactured dental prosthesis can be an
anterior crown. The anterior crown can be determined to be a
defective product if the standard deviation of the offset values of
the differences model is above 20 microns.
The differences model can be generated by comparing the design
model and the scanned model and then omitting from the differences
model data points below a margin line of the differences model. The
margin line can have a height ranging between 10-20% of the overall
height of the manufactured dental prosthesis. The surface area
below the margin line can be very difficult to scan accurately
because of at least the presence of a sharp apex. Inaccurate scan
data can lead to false positive detections of bad parts. For this
reason, data points of the surface area below the margin line are
omitted from statistical analysis (e.g., standard deviation
calculation, distribution analysis).
The occlusal surface of the manufactured dental prosthesis can also
be omitted from the differences model. Occlusal surfaces can have
very high variations due to their complex geometry of grooves,
ridges, cusps, and pits. For example, certain surface features have
radii that are smaller than a minimum radius a milling machine can
produce. These small differences because they are a concavity are
not significant in the function of the manufactured prosthetic
(e.g., a crown) but they would tend to skew the statistical
results. As such, data points of the occlusal surface can be
omitted from the data set used to perform statistical analysis as
occlusal surface data would great skew the statistical results.
When the manufactured dental prosthesis is a dental bridge, the
occlusal surface of each tooth of the dental bridge can be
omitted.
In a dental bridge, data points of surfaces between any two
adjacent teeth of the bridge can be omitted. Similar to the
occlusal surfaces discussed above, these interproximal surfaces
typically have radii that are smaller than a minimum radius a
milling machine can produce, and these small differences are not
essential to the function and fit of the dental bridge.
Accordingly, to achieve a more reliable quality control process,
data points from interproximal surfaces of dental bridge
manufactured prosthetics can be omitted from the differences
model.
The surface areas of the differences model where sprues are located
can also be omitted from the differences model. In other words,
data points within the sprue areas can be omitted from the data set
used to perform quality control analysis. If not omitted, data
points within the sprue areas can cause large localized deviation
and will skew the statistical results. A sprue location is where a
sprue is placed during the milling process to secure the
manufactured dental prosthesis within a milling block. By design,
sprue locations are not placed in functional areas (e.g., contact
regions) and/or close to the occlusal table.
Also disclosed is a system for performing quality control on a
dental prosthesis, the system comprises a quality control module
configured to determine whether the dental prosthesis is a good or
a defective product based at least on a differences model.
The differences model can be generated by comparing a design model
and a scanned model of the manufactured dental prosthesis. The
quality control module can be configured to determine whether the
dental prosthesis is a good or defective product by determining a
standard deviation of a difference value of points in the
differences model.
In some embodiments, the quality controller can determine that the
manufactured dental prosthesis is either too small or large based
on the distribution of differences. If the distribution is
negatively biased, when compared to a normal distribution, the
manufactured dental prosthesis can be deemed too small. If the
distribution is positively biased, when compared to a normal
distribution, the manufactured dental prosthesis can be deemed too
large.
The quality controller can also determine that the manufactured
dental prosthesis has a step when a distribution of differences
includes peaks at a left and/or right side of a normal distribution
curve. The system may further include a scanner for scanning the
manufactured dental prosthesis and for generating the scanned 3D
data model.
In yet another embodiment, a second method for performing quality
control on a dental prosthesis is disclosed. The method includes:
generating a design model of a dental prosthesis; manufacturing the
dental prosthesis based on the generated design model; scanning the
manufactured dental prosthesis to create a 3D scan model;
generating a differences model by comparing the generated CAD with
the 3D scan model; and determining whether the manufactured dental
prosthesis is a good or a defective product based at least on a
differences model.
In yet another embodiment, the systems and methods for performing
quality control discussed herein are used as a feedback loop to
detect machines, materials, operators, and other system and method
components that may be performing in an unintended manner. The
distribution of failures for all population effects are monitored
and flagged with automated identification and shutdown of any
machinery, operators, or systems that are determined to be
performing below an intended level. In still further embodiments,
the central tendency of each distribution of failures is monitored
and used to adjust any appropriate nominal operating values for the
affected machines, with an expected result of having fewer defects
overall.
The features and advantages described in the specification are not
all inclusive and, in particular, many additional features and
advantages will be apparent to one of ordinary skill in the art in
view of the drawings, specification, and claims.
Moreover, it should be noted that the language used in the
specification has been principally selected for readability and
instructional purposes and may not have been selected to delineate
or circumscribe the disclosed subject matter.
BRIEF DESCRIPTION OF THE DRAWINGS
The details of the subject matter set forth herein, both as to its
structure and operation, may be apparent by study of the
accompanying figures, in which like reference numerals refer to
like parts. The components in the figures are not necessarily to
scale, emphasis instead being placed upon illustrating the
principles of the subject matter. Moreover, all illustrations are
intended to convey concepts, where relative sizes, shapes and other
detailed attributes may be illustrated schematically rather than
literally or precisely.
FIG. 1 is a high-level block diagram of a system for manufacturing
dental prosthesis in accordance with some embodiments of the
present disclosure.
FIG. 2 is a block diagram of a quality control system in accordance
with some embodiments of the present disclosure.
FIG. 3 illustrates a scanner in accordance with some embodiments of
the present disclosure.
FIGS. 4A and 4B illustrate examples of differences models of two
different dental prostheses in accordance with some embodiments of
the present disclosure.
FIG. 5A is a graph illustrating the offsets distribution of a
differences model in accordance with some embodiments of the
present disclosure.
FIG. 5B is a spreadsheet listing the offsets distribution of the
differences model of FIG. 5A.
FIG. 6A is a graph illustrating the offsets distribution of a
differences model in accordance with some embodiments of the
present disclosure.
FIG. 6B is a spreadsheet listing the offsets distribution of the
differences model of FIG. 6A.
FIGS. 7 and 8 are example graphs illustrating the offsets
distribution of differences models in accordance with some
embodiments of the present disclosure.
FIGS. 9-11 illustrate differences models of anterior crowns
generated in accordance with some embodiments of the present
disclosure.
FIG. 12 is a flow diagram of a quality control process in
accordance with some embodiments of the present disclosure.
FIG. 13 illustrates a dental crown and a reliable data band in
accordance with some embodiments of the present disclosure.
FIG. 14 is a side view of a differences model of a dental bridge
with a proposed margin line generated in accordance with some
embodiments of the present disclosure.
FIG. 15 is a front view of the differences model shown in FIG.
14.
FIG. 16 is a perspective view of the differences model shown in
FIG. 14 with proposed occlusal surface boundaries generated in
accordance with some embodiments of the present disclosure.
FIG. 17 illustrates a modified differences model with surfaces
in-between two neighboring teeth being omitted in accordance with
some embodiments of the present disclosure.
FIG. 18 illustrates a modified differences model with sprue areas
being omitted in accordance with some embodiments of the present
disclosure.
FIGS. 19A-D are example graphs illustrating the offsets
distribution of differences models in accordance with some
embodiments of the present disclosure.
FIGS. 20, 21, 22A, and 22B are perspective view of a dental
prosthesis fixture in accordance with some embodiments of the
present disclosure.
FIG. 23 is perspective view of another dental prosthesis fixture in
accordance with some embodiments of the present disclosure.
FIG. 24 is a system diagram that can be used to implement the
system and method for performing quality control in accordance with
some embodiments of the present disclosure.
DETAILED DESCRIPTION
In the following description, for purposes of explanation, numerous
specific details are set forth to provide a thorough understanding
of the invention. However, it will be apparent to one skilled in
the art that the invention can be practiced without these specific
details. In other instances, structures and devices are shown in a
block diagram form in order to avoid obscuring the invention.
Overview
To better understand the quality control process, an overview of an
exemplary dental prosthesis manufacturing process is provided. FIG.
1 illustrates a system 100 for manufacturing custom designed dental
prostheses in a continuous automated process in accordance with
some embodiments of the disclosure. Information concerning custom
dental prostheses can be received by a dental prosthesis management
system 102 that is in communication with an automated manufacturing
system 100. Dental prosthesis management system 102 can be locally
or remotely located. Additionally, one or more functionalities
(modules) of dental prosthesis management system 102 can reside
locally or remotely. For example, a local tracking and inspection
module can be part of dental prosthesis management system 102, and
a plurality of dentition databases (not shown) can be located on
the cloud.
System 100 includes a plurality of process stations such as a
milling center 105, a separating station 110, a scrap disposal
station 115, and a transfer and inspection/tracking station 150.
Milling center 105 can mill material blocks and form custom dental
prostheses according to design specifications, which can be
obtained from dental prosthesis management system 102. Separating
station 110 is provided for separating workpieces into milled
custom dental prostheses and remnant material blocks. Scrap
disposal station 115 can be provided to remove and eliminate
remnant material blocks from further processing.
System 100 may include a transfer system, such as a conveyor system
120 that comprises one or more conveyor units, automatically and/or
simultaneously transfers a plurality of custom dental prostheses
between remaining, post-milling process stations. Each process
station may comprise a different transfer unit, or a different
conveyor suitable to the environmental conditions of the process.
Optionally, additional process stations may be included in the
automated system, including an oven 125 for thermal treatment, and
a cooling unit 130.
A carrier or container 155 may be provided to move material blocks
and dental restorations between processing units. In one
embodiment, container 155 may have a structure that is configured
to interface with each process station, including individual
pockets or compartments to separate and track a plurality of custom
milled workpieces for simultaneous processing into custom dental
prostheses in a hands-free and automated process. Container 155 can
include a plurality of pockets to hold a plurality of workpieces in
a specified location and orientation for processing through the
plurality of process stations. Each station, such as separating
unit 110 and scrap disposal unit 115, may comprise devices having
components in spaced arrangements that align with the tray pockets
and with the orientation of workpieces held within the pockets. The
assignment of an individual workpiece to a specific tray pocket
isolates each workpiece and identifies the custom dental prostheses
throughout the automated process until removal of the prostheses
from the tray, for accurate association of each custom dental
prosthesis with corresponding dental prosthesis information.
Dental prosthesis management system 102 may receive dental
prosthesis information associated with a proposed custom dental
prosthesis to be processed by milling center 105.
Dental prosthesis management system 102 may comprise a system
capable of performing tasks related to the manufacture of dental
prostheses, and can be implemented on a computer system, such as a
server. Dental prosthesis management system 102 may include a
module for selecting dental prostheses, a machining instructions
tool, one or more cameras, one or more sensors, and a dental
prosthesis database. The machining instructions tool, in turn, may
include more than one database for storing information related to
the modules or materials used within the system and information
pertaining to the custom dental prosthesis, and machining
instructions. Databases may be internal to dental prosthesis
management system 102, located on an external device connected to
dental prosthesis management system 102, or located remotely, such
as in cloud-based storage.
Information used to design and/or manufacture a dental prosthesis
for a patient may be received by dental prosthesis management
system 102 from a dentist or dental office. In some representative
examples, a dentist or dental office will provide information
concerning the oral situation of a patient, such as a physical
impression or an electronic file containing a digital scan of the
patient's oral situation. Additionally, the dentist or dental
office may also provide instructions for the material or materials
to be used to manufacture the prosthesis, the type and construction
of the prosthesis, the shade or other aesthetic features for the
prosthesis, and the like. As used herein, the term "dental
prosthesis" refers to any dental restorative including, without
limitation, crowns, bridges, dentures, partial dentures, implants,
onlays, inlays, or veneers.
In some embodiments, information regarding the selected material
block is used for calculating machining instructions and is stored
in a database of dental prosthesis management system 102. For
example, material blocks that undergo dimensional reduction after
milling and sintering are associated with material-specific
information in order to accurately calculate machining instructions
to derive the dimensions of an enlarged prosthesis milled from a
pre-sintered block. The information regarding the material
properties of the specific material that is used in the milling
calculations may be associated with the material and stored in a
database until the material block is selected and the information
is retrieved.
After all machining steps are completed, the workpiece may be
removed from the mill manually, or by a robotic handler 116. In one
embodiment, robotic handler 116 loads a plurality of custom
workpieces from a single mill or a plurality of mills (e.g., mills
112, 113, 114, and 115) onto container 155.
Quality Control
System 100 can include one or more quality control stations 200.
For example, quality control station 200 can be placed immediately
after the milling process to determine whether the milled dental
prosthesis is of proper size (taken into account the enlargement
factor of the milling block) or has milling defects such as steps.
Quality control station 200 can also be placed after the sintering
process, this allows quality control station 200 to check the final
size (and other parameters) of the dental prosthesis after the
sintering process. Alternatively, quality control station 200 can
be placed both locations--after the milling and sintering
processes.
Quality control station 200 can also be communicatively linked to
dental prosthesis management system 102. This enables quality
control station 200 to determine the enlargement factor (EF) of the
milling block that will be used to form a dental prosthesis. Taking
the EF factor into account, quality control station 200 can
determined whether a milled dental prosthesis is of the proper
size. For example, if quality control station 200 determines that
the milled dental prosthesis has the same size (or smaller) as the
design model (received from dental prosthesis management system
102) of the same dental prosthesis, then the part can be flagged
for inspection and/or rejection. In this way, the defective-milled
dental prosthesis does not have to go through the sintering
process. A design model can be a computer-generated model (e.g.,
CAD model, 2D calibrated model) generated based on a patient's
specific data (e.g., dentition, mold impression). The design model
can also be a CNC (computer numerical control) simulated model or a
3D printing simulated model. The CNC simulated model can be a
virtual model generated from a simulated CNC process. Similarly,
the 3D printing simulated mode can be a virtual model generated
from a simulated 3D printing process.
The sintering process starts at transfer-tracking station 150,
where one or more milled dental prostheses are transferred to a
sintering tray 165. As mentioned, quality control station 200 can
also be placed after the sintering process in order to perform
quality control on the sintered dental prosthesis. In this way,
various defects such as improper size, cracks, chips, steps, etc.,
can be detected and quantified. In some embodiments, system 100 can
have two quality control stations, one after the milling process
and one after the sintering process.
FIG. 2 illustrates the quality control station 200 in accordance
with some embodiments of the present disclosure. Quality control
station 200 includes a scanner 205, a modeling module 210, and a QC
module 215. Scanner 205 can be a contact or non-contact inspection
device that can generate a scanned data model of the scanned
object. The scanned data model can be a 3D model or 2D calibrated
model. Scanner 205 can use light or radio waves having wavelengths
at any point or range in the electromagnetic spectrum suitable to
scan a dental prosthesis. In some embodiments, the light used to
scan the dental prosthesis can have a wavelength range between
400-500 nm. Because a finished dental prosthesis is glazed, it can
be semi-translucent and thereby can affect the way light is
reflected. To address this issue, in one embodiment, the dental
prosthesis can be pre-heated so that it will emits radiation in the
infrared (IR) region of the radio spectrum. In this embodiment,
scanner 205 can use light with wavelength in the IR region to scan
the pre-heated dental prosthesis.
Scanner 205 can generate a 3D data set of the scanned dental
prosthesis in a stereolithography CAD format known as STL. Scanner
205 can also generate other types of 3D data set format such as
3DS, BLEN, SCL, SKP, raw point output, or any other type of format
as required by modeling module 210. In some embodiments, scanner
205 can scan a dental prosthesis and generate a 2D calibrated
image. To generate the 3D data set, scanner 205 can use 3D scanning
technology such as, but not limited to, laser triangulation,
structured light, laser profilometry, focus variation, optical
coherence tomography (OCT), conoscopic holography, confocal
microscopy, computed tomography (CT), contact measurement, and
photogrammetry.
Modeling module 210 can include 2D/3D best-fitting algorithms to
best-fit spatial points of a design data set (e.g., CAD data set,
calibrated 2D data set, CNC simulated data set, 3D printing
simulated data set) of a dental prosthesis to the corresponding
best-fitting spatial points of the scanned 3D data set of the same
dental prosthesis. Modeling module 210 can also generate a
differences model based on the best-fitting results. A differences
model can have the same number of spatial data points as the design
data set and/or scanned 3D data set. In the differences model, each
data point can be an offset between the design data set and the
scanned 3D data set. In an example of a CAD data set, a zero offset
means that the point in the scanned 3D data set is in the exact
location as the corresponding best-fitting point in the CAD data
set. In other words, if the differences model comprises of all zero
offset points, then the scanned 3D data set is exactly the same as
the CAD data set.
A differences model can also be generated by comparing data points
of other type of design data set such as CNC or 3D printing
simulated model with data points of the scanned 3D data set.
QC module 215 can include a data preprocessing module (not shown)
configured to pre-process the data set of the differences model
before statistical analysis can be performed to determine whether
the prosthesis is a defective or a good product. The data
preprocessing module can be an integrated component of QC module
215 or can be an independent module that can be called (via an
application programming interface) to preprocess the differences
model. The data preprocessing module can include dentition modeling
software and graphical user interfaces (GUI) that enable a user to
select various locations on the differences model and their
corresponding data for omission to generate a reliable band of data
on which statistical analysis can be performed at a later stage.
For example, using the GUI of the data preprocessing module, the
user can adjust a computer-generated boundary of the margin line by
moving one or more points of the margin line. In this way, the user
can define the margin surface as desired. In another example, the
data preprocessing module can propose the occlusal surface to be
omitted from the differences model. Using the GUI, the user can
reject, accept, or modify the proposed occlusal surface.
The data preprocessing module can modify the differences model to
generate a modified differences model based on the type of
prosthesis (e.g., posterior crown, anterior crown, bridge) being
analyzed. For example, the preprocessing module can generate a
modified differences model for a posterior crown by using a set of
rules that is different from a set of rules used to generate a
modified differences model for an anterior crown. The preprocessing
module can also use yet another different set of rules to generate
a modified differences model for a bridge. The preprocessing module
can use different set of rules to generate differences models for
anterior and posterior bridges. In some embodiments, the
preprocessing module can use the same set of rules to generate
differences models for anterior and posterior bridges.
A modified differences model is a 3D differences model with its
data set being truncated or deleted at various portions that
correspond to certain locations of the unmodified 3D differences
model. In other words (from a 3D perspective), a modified
differences model is 3D differences model that is trimmed at
various locations of the 3D differences model. For example, the
original (e.g., unmodified) differences model can include data of
the occlusal surface and/or the margin area of a crown. In a
modified differences model, the data of the occlusal surface and/or
the margin area can be deleted and therefore are not included in
the statistical analysis. The omission of the occlusal surface
and/or the margin area creates a band of data representing the
modified differences model that can be reliably used to perform
statistical analysis. A margin area is an area of a tooth near or
adjacent to the gingival margin.
When performing quality control on anterior crowns, QC module 215
can use a specific set of rules for anterior crowns to generate the
modified differences model (e.g., a model with a band of good
data). In some embodiments, QC module 215 can omit data points
located on the lingual and buccal surfaces of the differences model
of the anterior crowns. Data points within the boundary regions
between the lingual and mesial, mesial and buccal, buccal and
distal, and lingual and distal can either be omitted or included in
the modified differences model. In some embodiments, the boundary
regions can be included in the modified differences model of an
anterior crown. This process can generate a band of data covering
the distal and mesial surfaces of the differences model for
anterior crown. In an anterior crown, the manufacturing tolerances
of the distal and mesial surfaces are tightly controlled because
they are adjacent surfaces to neighboring teeth. If the tolerances
in these areas are not met, the crown may not fit because it can be
too large or too small.
For posterior crowns, QC module 215 can omit data points located on
the occlusal surface and/or the margin surface of the differences
model. This generates a reliable band of data for statistical
analysis by eliminating potentially high variability surfaces.
For posterior bridges, QC module 215 can omit data points located
on the occlusal, margin, and sprue areas of each crown of the
bridge. Data points of the intersection areas of the bridge can
also be omitted. An intersection area is an area between the two
crowns of a bridge. Tolerances in the intersection areas are not
important and can be omitted from the differences model. A sprue
area is an area on a crown where a sprue is located to secure the
bridge within the milling mold. A bridge can have two or more
sprues, typically three sprues are used to secure the bridge within
the milling mold. Once the milling process of a bridge is
completed, the sprues are broken off and the sprue areas on the
bridge are manually sanded and smoothened. Because of the manual
sanding and smoothening process, which can be highly variable, the
sprue areas can be omitted from the differences model to generate a
modified differences model.
In some embodiments, a posterior crown, anterior crown, or bridge
is manufactured using wax or other removable support material such
that a sprue is not needed or formed during the machining process.
See, e.g., the manufacturing methods described in United States
Patent Application Publication No. 2017/0156828, which is
incorporated by reference herein. When a sprue is not formed on the
body of the manufactured prosthesis, there is no need to omit data
points associated with the sprue area, and more of the surface area
of the manufactured prosthetic can be incorporated into the
differences model.
QC module 215 can analyze the differences model and/or the modified
differences model to determine the types of defects present in the
manufactured dental prosthesis. In some embodiments, QC module 215
is configured to analyze the modified differences model to perform
quality control. QC module 215 can quantify the defects by
quantifying the level or severity of the defects. Exemplary types
of defects include improper size (e.g., enlargement factor (EF)),
chips, cracks, steps, indentations (e.g., CAM error), etc. Some
defects such as cracks and chips can be visible to the human eye.
However, defects such as improper size, small indentations, and
steps can be very difficult (if not impossible) to visually detect
with the human eye. For example, a crown can be too small just by
50 microns under the tolerance. This type defect would be almost
impossible to perceive by a human inspector. An alternative manual
inspection method is to use a Vernier caliper to check the size of
the crown at various locations. However, this would be very
inefficient and costly in term of the human hours required. In
another example, a dental prosthesis can have a step having a
height of 50 microns. Even though the resolution of the human eye
is higher than 50 microns, a step of this size is extremely hard to
spot because it can blend in with the surrounding surfaces of the
dental prosthesis. In some embodiments, a step of 30 microns is
acceptable.
QC module 215 can also compare a CNC or 3D simulated model to a CAD
design model of a dental prosthesis to generate a differences
model. In this way, any kinematic and/or machine milling
constraints can be identified by the differences model. The
identified constraints can then be used to modify the CAD design
model to improve the design of the dental prosthesis. By using
simulated models (e.g., CNC simulated, 3D simulated) the need to
make the physical model and scanning the physical model to generate
a 3D scanned data set can be optionally bypassed. Alternatively,
comparing simulated models with design models can be used as an
additional QA step.
In some embodiments, QC module 215 can determine whether the
scanned dental prosthesis is a good or defective part based on the
distribution of offsets of the modified differences model (e.g.,
band of data). For example, in a good part, all of the offsets have
a distribution that is similar to a normal distribution. In a
defective part, the distribution of the offsets is biased in the
negative or positive direction. A negative direction is toward the
negative side from the center of the normal distribution.
Alternatively, a defective part can have a distribution with one or
more peaks in the negative or positive direction. In this way, QC
module 215 can determine whether a part is too small or large or
whether it has steps. A more detailed discussion on statistical
analysis is provided below.
It should be noted that one or more functions and/or features of
modeling module 210 and QC module 215 can be integrated into
scanner 205. Similarly, one or more functions of QC module 215 can
be integrated into modeling module 210 or vice versa. For example,
if all functions and/or features of modeling module 210 and QC
module 215 are integrated into scanner 205, then scanner 205 can
also generate the differences model and perform statistical
analysis on the differences model to determine whether a
manufactured dental prosthesis is a good or defective part.
QC module 215 can also provide a feedback loop to detect machines,
materials, and operators that may be performing at an unexpected or
unintended performance level, and then initiate corrective actions.
For example, in some embodiments, the QC module 215 will initiate
shutdown of one or more manufacturing machineries based at least on
one or more results of one or more statistical analysis. For
example, if QC module 215 detects that the distribution of
differences has more two or more peaks--which is an indicator of a
step--QC module 215 can initiate shutdown of one or more
machineries in the milling process. In another example, if QC
module 215 detects that the distribution of differences is
positively biased--which is an indicator that the crown is too big
and could be related to the crown's EF--QC module 215 can initiate
shutdown of one or more machineries that are responsible for
generating the milling block. QC module 215 can also send data
relating to the defective part back to dental prosthesis management
system 102 for tracking. For example, QC module 215 can collect
data relating to the defective crown such as, but not limited to,
the crown ID, milling block ID, milling machine ID, sintering
profile ID, and send them back to dental prosthesis management
system 102. In still further embodiments, the QC module 215
monitors the central tendency of each distribution of failures and
uses the monitored values to adjust any appropriate nominal
operating values for the affected machines, with an expected result
of having fewer defects overall.
FIG. 3 illustrates scanner 205 in accordance with some embodiments
of the present disclosure. Scanner 205 includes one or more sensors
210 and 215 and a rotatable holder 220. Each of the sensors can use
blue light, which can have a wavelength between 450-495 nm. It
should be noted that other wavelengths can also be used by scanner
205. In some embodiments, scanner 205 can have two sensors. The
first sensor 210 can scan the dental prosthesis from the top at
approximately 45 degrees angle. The second sensor 215 can scan the
dental prosthesis from a negative angle with respect to the main
horizontal surface of scanner 205. The negative scanning angle can
range between 15-30 degrees. By having sensors 210 and 215
positioned at a positive angle and a negative angle, respectively,
an accurate scan can be achieved.
Rotatable holder 220 can be air actuated to open up its fingers to
hold the inside of a dental prosthesis. For example, a dental crown
prosthesis typically has a void in the center. The void is where
the dental crown will be installed to a prepared site having a
corresponding tooth structure to mate with the void. The dental
crown can be secured to the rotatable holder by having the fingers
of the rotatable holder open outward and pressing against the
inside wall of the void. Alternatively, the dental crown can be
held in place using putty and/or adhesive.
Rotatable holder 220 can rotate 30 degrees or more for each
scanning cycle. For example, rotatable holder 220 can rotate by 30
degrees 12 times in order to achieve a full rotation. After each
rotation, rotatable holder 220 can pause for several seconds to
allow sensors 210 and 215 to fully scan the section facing the
sensors. In another example, rotatable holder 220 can rotate by 60
degrees 6 times or by 90 degrees 4 times, etc.
FIGS. 4A and 4B are examples differences model of a posterior crown
generated by modeling module 210 in accordance with some
embodiments of the present disclosure. FIG. 4A illustrates a
differences model of a posterior crown 400 that passed the QC
inspection process. As shown, posterior crown 400, which is
indicated by the color green. Points within the green region (e.g.,
band of data) have a small offset that is within a predetermined
tolerance (e.g., .+-.50 microns). In other words, the offsets
between points in the CAD data set and the corresponding
best-fitting points on the scanned 3D data set are below a given
tolerance threshold in the green region. The tolerance threshold
can range between 30 to 70 microns, depending upon the dental
prosthesis being manufactured and patient's specification. In FIG.
4A, manufactured dental prosthesis 400 can be considered to be a
good part when 70-90% (e.g., 75%, or 80%, or 85%) of the offsets
(including zero offset) are within the tolerance threshold of
.+-.50 microns. In some embodiments, QC module 215 can identify a
manufactured dental prosthesis to be a good part when 85% or more
of the offsets are within the tolerance threshold. Additionally, QC
module 215 can require that all offsets greater than .+-.80-95
microns must account for less than a threshold percentage (e.g.,
5%, or 3%, or 1%) of the differences distribution. In other words,
as an example using a 1% threshold, out of 50,000 data points
(offsets) in the differences model, there can be no more than 500
offsets greater than 80-95 microns. In some embodiments, all
offsets greater than .+-.88 microns must account for less than 1%
of the differences distribution in order for the manufactured
dental prosthesis to be considered a good part.
The .+-.50 microns tolerance threshold and the 85% percentage
threshold are determined based on empirical statistical studies to
provide a balance of high quality and high yield rate. As noted,
both the tolerance and percentage thresholds can be adjusted to
meet 3-sigma engineering tolerances as required. For example, the
percentage threshold can be adjusted to 95.45% to meet 3-sigma
quality requirements. Additionally, the second tolerance threshold
of .+-.88 microns is also selected based on empirical statistical
studies to achieve high quality and high yield rate. Per 3-sigma
requirements, in some embodiments, the second tolerance threshold
can be adjusted to .+-.75 microns.
FIG. 4B illustrates an exemplary differences model of a
manufactured dental prosthesis 450 that fails quality control. As
indicated by the color legend, a substantial number of points on
dental prosthesis 450 are outside of the tolerance threshold of
.+-.50 microns. Specifically, dental prosthesis has a substantially
number of points above +88 microns. This indicates that dental
prosthesis 450 is too large. This could mean there is an error in
the milling process, the sintering process, the EF calculation
process, or a combination thereof. In some embodiments, an EF
variation of .+-.30 microns is acceptable.
FIG. 5A illustrates an example distribution 500 of differences or
offsets of dental prosthesis 400 that passes quality control. As
illustrated, distribution 500 has a normal distribution where
substantially all of the differences are within .+-.40 microns.
This is well within the tolerance threshold of .+-.50 microns.
Additionally, distribution 500 has a normal bell curve shape
without any peaks at the outer edges (i.e., left and right sides of
the bell curve). FIG. 5B is a spreadsheet listing the difference
values of all sampling points in distribution 500. In FIG. 5B, the
.+-.50 microns range is indicated by bracket 510. The total number
of points within .+-.50 microns is over 94% of the total number of
points. Each point is a difference (or offset) between a point in
the CAD data set and the corresponding best-fitting point in the
scanned 3D data set.
QC module 215 can analyze the distribution of differences to
determine whether a part is good or defective. Prior to analyzing
the distribution of offsets of the differences models, QC module
215 can eliminate a certain portion of the differences model from
analysis. For example, QC module 215 can eliminate the top portion
of a dental prosthesis. In a crown, the top portion is near the
occlusal surface of the crown. QC module 215 can also eliminate a
bottom portion of the crown proximal to the margin line (the bottom
portion of the crown near the gum line of the patient once the
crown is mounted). By eliminating the top and bottom portions from
analysis, a reliable band of data (in the middle of the crown) can
be generated. In this way, a more accurate statistical analysis can
be performed. See FIGS. 9 and 10 for more discussion on the
elimination of the top and bottom portions to create a reliable
band of data.
In some embodiments, the percentage threshold is 85% and the
tolerance threshold is .+-.50 microns. In other words, if 85% of
all points are within .+-.50 microns, then the part can be
considered to be a good part. The percentage threshold can range
between 75% to 96%, depending upon the type of dental prosthesis to
be manufactured, the milling block material, patient's
specifications, etc. The tolerance threshold can have a range
between 30 to 70.+-. microns.
In some embodiments, QC module 215 can require a dental prosthesis
to pass two sets of percentage and tolerance thresholds. The first
set of percentage-tolerance thresholds can require all points
within .+-.50 microns must account for greater than 85% of the
total number of points. The second set of percentage-tolerance
thresholds can require all points greater than .+-.88 microns must
account for less than 1% of the total number of points. Thus, in
order to pass quality control, a manufactured dental prosthesis
must meet both sets of percentage-tolerance thresholds. For
example, if a dental prosthesis meets the first set of
percentage-tolerance threshold but fails the second set, then it
can be classified as a defective part. It should be noted that a
distribution of a differences model such as distribution 500 can be
generated by modeling module 210 and/or QC module 215.
FIG. 6A illustrates an example distribution 600 of a differences
model of dental prosthesis 450 that fails quality control. In
distribution 600, the majority of the offsets fall between .+-.80
microns. Referring to FIG. 6B, which is a spreadsheet listing all
offsets in distribution 600, approximately 79% of all points fall
within the tolerance threshold of .+-.50 microns as indicated by
bracket 620. This alone can result in the dental prosthesis being
classified as a defective part. Additionally, distribution 600
includes two outer peaks 610 and 615 in FIG. 6A. This can indicate
a step on the surface of dental prosthesis 450.
Referring again to FIG. 6B, distribution 600 does not meet the
second percentage-tolerance thresholds requirement, which is less
than 1% of points are larger than .+-.88 microns. Brackets 625 and
630 indicate a substantial number of points (much larger than 1% of
the total number of points) are larger than .+-.88 microns.
Accordingly, based on this distribution of differences, QC module
215 can classify this part as a defective part.
QC module 215 can also quantify the defect by determining whether a
distribution has more than one peaks. In distribution 600, there
are two peaks, one on each side of the normal distribution. As
mentioned, these peaks indicate the present of a step on the
surface of dental prosthesis 450. To quantify the steps, QC module
215 determines the point in the distribution where the percent of
points starts to increase again, starting from the middle (zero
deviation). In distribution 600, the location where percent of
points starts to increase again is at 635 and 640. For example, at
635, the percent of points went from 0.351% to 0.374%. This
increase reverses the decreasing trend. For example, starting in
the middle at negative 10 microns, the percent of points within
negative microns is 19.036%. From there moving up the spreadsheet
(in the negative direction), the percent of points decreases as we
move up the spreadsheet. At 635, the percent of points started to
increase again. In some embodiments, QC module 215 can classify the
step based on where the percent increase occurs in the
distribution. At 635, the offset value is 196 microns. Similarly,
at 640, where the percent of points increases from 0.291% to
0.497%, the offset value is also 196 microns.
FIG. 7 illustrates a distribution 700 of a differences model of a
dental prosthesis that can be classified as being too small by QC
module 215. To determine whether a manufactured dental prosthesis
is too small or large, QC module 215 can analyze a distribution to
determine whether the distribution is heavily biased toward the
negative or positive side of the distribution curve. In FIG. 7,
distribution 700 is heavily biased toward the negative side. In
some embodiments, QC module 215 can classify a part to be a
defective part if it is biased toward the negative or positive side
of the curve. Additionally, QC module 215 can classify the dental
prosthesis having distribution 700 to be a defective part because
it appears that greater than 1% of the total points are outside of
the .+-.88 microns.
FIG. 8 illustrates a distribution 800 of a differences model of a
manufactured dental prosthesis that can be classified as being too
large by QC module 215. In distribution 800, a substantial number
of points are located on the positive side of the distribution
curve. This means that the dental prosthesis is likely too large.
If the manufactured dental prosthesis (e.g., a crown) is too large,
it would not fit properly into the prepared area inside the
patient's mouth, e.g., the crown would not fit between the adjacent
teeth or would have high occlusion. QC module 215 can also quantify
the magnitude of size defect (too small or too large) by
determining the percent of points in the biased portion of the
curve. The average, mean, or median value of those points can be
determined to quantify the size of the defect.
FIG. 9 illustrates a differences model of anterior crown 900, which
can be generated using QC module 215 by comparing the distance of
each point of the CAD model of the anterior crown and the 3D
scanned model of the manufactured anterior crown, which was
produced based on the CAD model. As shown, the differences model of
crown 900 can include data points from buccal region 905. In some
embodiments, data points from upper buccal region 910 and/or upper
lingual region (opposite side of buccal region) can be omitted from
the differences model to generate a modified differences model
(e.g., a reliable data set on which statistical analysis can be
performed). Typically, the upper lingual and buccal regions of an
anterior crown are not crucial surfaces for crown fitting.
Accordingly, tight tolerances in these regions are not required.
For example, if statistical analysis is performed on the entire
differences model, a good crown may be needlessly rejected because
a slight defect in the upper lingual surface is present. The same
crown can otherwise be an almost perfect part in other regions of
the crown.
In some embodiments, the lower portion of buccal region 905 can be
included in the modified differences model. As previously
mentioned, a modified differences model is a differences model with
a portion of the data set being trimmed. Alternatively, lower
portion of buccal region 905 can be omitted from the differences
model. In this sample, the differences model of crown 900 has high
standard of deviations in both lower buccal region 915 and mesial
region 920 of crown 900. The standard deviation of data points
(offset values of points of the differences model) in the
highlighted region 925 is 77 microns. Based on this high standard
deviation, this crown 900 can be rejected as a bad crown. In some
embodiments, QC module 215 can reject any crown if the standard
deviation of points in the differences model in any essential
regions of a crown is above 20 microns. Essential regions of an
anterior crown can be the mesial surface, the distal surface,
and/or the lower portions of the buccal and lingual surfaces. QC
module 215 can determine whether a crown is good based only the
standard deviation of points on the mesial and distal surfaces of
the differences model (or modified differences model).
FIG. 10 illustrates a differences model of anterior crown 1000.
Crowns 900 and 1000 are both manufactured from the same CAD file.
As shown crown 1000 has much less variation as compared to crown
900. In this sample, the standard deviation of the mesial region
1005 is approximately 22 microns, which is still above the normal
standard deviation threshold of 20 microns. It should be noted that
the standard deviation threshold can be adjusted and can have a
range between 10 to 30 microns. The source of the variations can be
variability of enlargement factor within the material (e.g.,
milling stock) and/or incorrect calculation of the EF of the
milling stock.
FIG. 11 illustrates a differences model of anterior crown 1100,
which was produced from the same CAD file as crowns 900 and 1000.
As revealed by statistical analysis on the differences model,
anterior crown 1100 has a CAM defect at region 1105. This type of
defects is typically very hard to spot via usual inspection. The
standard deviation of the difference values (e.g., the difference
value of points in the differences model) in region 1105 is 22
microns, which is practically impossible to perceive with the human
eye. In some embodiments, the standard deviation threshold can be
set at 20 microns, which means crown 1100 can be considered as a
defective part.
FIG. 12 is a flow diagram of a quality control process 1200 in
accordance with some embodiments of the present disclosure. Process
1200 starts at block 1205 where a manufactured dental prosthesis is
scanned to generate a 3D data set. At block 1210 a differences
(offsets) model is generated by best fitting the scanned 3D data
set of the manufactured dental prosthesis with a CAD data set of
the same. A differences model is a collection of offset data
between points in the CAD model and corresponding best-fitting
points in the scanned 3D data set. An ideal differences model
comprises of zero offset points.
At block 1215, a band of data is generated prior to performing
statistical analysis on the distribution of differences of the
differences model or data set. The band of data is generated by
eliminating a top and/or a bottom portion of the differences model
from the statistical analysis. FIG. 13 illustrates a differences
model of a crown 1300 having the band of data in accordance with
some embodiments of the present disclosure. Crown 1300 includes an
occlusal surface 1305, a top portion 1310, a middle portion 1315,
and a bottom portion 1320. To generate a band of data 1350 that can
be reliably used for statistical analysis, points located in top
portion 1310 can be eliminated from the data set for statistical
analysis. In some embodiments, points located in both top portion
1310 and bottom portion 1320 can be eliminated from the data set
for analysis. Alternatively, only points in the top or bottom
portion are eliminated from the data set. After the points in the
top and bottom portions are removed (or simply not included in the
analysis), band of data 1350 is generated. Band of data 1350 is
reliable because it does not include variabilities inherent in
occlusal surface 1305 and along the margin line (the bottom edge of
bottom portion 1320). Additionally, a dental prosthesis can have a
height tolerance of .+-.30 microns. Thus, by eliminating bottom
portion 1320 from the statistical analysis, the height variation of
the dental prosthesis is removed. This reduces the overall
variability of the data set and thereby making it more
reliable.
Referring again to FIG. 12, after the band of data is generated,
the distribution of differences or offsets can be analyzed at block
1220. Empirical studies show that using only data from the modified
differences model (e.g., data in band 1350) yield more accurate and
consistent statistical results than using the entire differences
data set of the generated differences model (at 1210).
At block 1225, various types of defect can be identified and
quantified based on the analysis of the distribution of the
differences data set. For example, crown 1000 can be classified as
being too small if its differences distribution is negatively
biased. Alternatively, crown 1000 can be classified as being too
large if its differences distribution is positively biased. In
another example, a step located in middle portion 1315 (see FIG.
13) can be identified if the distribution has two or more peaks.
The step can also be quantified by analyzing where the distribution
starts to increase on the left or right side of the distribution
curve. For example, referring to FIGS. 6A and 6B, a step can be
identified by the presence of peaks 610 and 615. Additionally,
peaks 610 and 615 can be quantified by recognizing where in the
distribution the percent distribution of points started to reverse
and increase. In FIG. 6B, the point of percentage reversal for peak
610 occurs at 196 microns. Accordingly, the step can be quantified
to have a height of approximately 200 microns.
Referring again to FIG. 2, modeling module 210 can contain codes,
instructions, and algorithms which when executed by a processor
will cause the processor to perform one or more functions described
in process 1200 such as, but not limited to: perform a best fit of
the CAD model of a dental prosthesis and a scanned 3D model of the
same dental prosthesis (see block 1210 of FIG. 12); and to generate
a differences model based on the best fit (see also block
1210).
QC module 215 can contain codes, instructions, and algorithms which
when executed by a processor will cause the processor to perform a
best fit of the CAD model of a dental prosthesis and a CNC or 3D
printing simulated model.
QC module 215 can contain codes, instructions, and algorithms which
when executed by a processor will cause the processor to perform
one or more functions described in process 1200 such as, but not
limited to: generate a modified differences model (e.g., band of
data 1350); analyze differences distribution (e.g., data
distribution of modified differences model), and identify and
quantify defects based on distribution analysis.
FIGS. 14-18 graphically illustrate a process 1400 for generating a
modified differences model in accordance with some embodiments of
the present disclosure. FIGS. 14-18 will be discussed concurrently.
FIG. 14 is a side view of a 3D differences model 1420 of a bridge
1450, which comprises teeth 1452, 1454, and 1456. Process 1400 can
start by defining margin line 1460 of bridge 1450. Margin line 1460
can be manually determined or can be automatically proposed by the
computer (e.g., QC module 215) based on at least the percentage
distance of the total height of the tooth away from the bottom of
the tooth. In other words, if the overall height of a tooth at a
certain location is 20 mm, the margin can be set at 15% of the
overall height at that location. This translates to a margin point
being located at 3 mm away from the base (e.g., bottom) of the
tooth. In some embodiments, the margin line can be 5-25% of the
overall height of the tooth. Once margin line 1460 is defined,
margin surface 1465 can be omitted from differences model 1420.
Margin surface 1465 can be defined the surface between margin line
1460 and bottom perimeter 1467 of bridge 1450. FIG. 15 illustrate
the front view of differences model 1420. Using a GUI, a user can
rotate the view to any angle and can manually adjust any point of
the proposed margin line 1460.
FIG. 16 illustrates the occlusal surface boundaries 1600A-C of
bridge 1450 generated in accordance with process 1400. Occlusal
surface boundaries 1600A, 1600B, and 1600C can be manually
generated or can be automatically generated by QC module 215.
Similar to margin line 1460, a user can use a GUI to change the
angle of view and adjust any point of occlusal surface boundaries
1600A-C. All data points within occlusal surface boundaries 1600A-C
can be omitted from the differences model to generate a modified
differences model. In other words, surfaces 1605A-C can be omitted
from differences model 1420. In this way, any variabilities in the
occlusal surface cannot affect the statistical analysis of bridge
1450, which can cause false negative rejection of the bridge. It
should be noted that the order of defining margin line 1460 and
occlusal surface boundary 1600 can be revered as the exact order is
not important.
In some embodiments, process 1400 can omit all data points of
surfaces in-between any two teeth of the bridge. For example, in
bridge 1450, in-between surfaces inside of bounding boxes 1610 and
1620 can be omitted from the differences model to generate a
modified differences model. Bounding box 1610 defines one or more
surfaces in between teeth 1452 and 1454. Bounding box 1620 defines
one or more surfaces in between teeth 1454 and 1456. QC module 215
can automatic select the one or more surfaces in bounding boxes
1610 and 1620 for omission. Using a GUI, a user can accept,
decline, or modified the proposed surfaces for omission.
FIG. 17 illustrates a modified differences model 1700 having a
plurality of surfaces omitted from differences model 1420. As shown
in FIG. 17, margin surface 1465, occlusal surfaces 1605A-C, and
surfaces within bounding boxes 1610 and 1620 have been omitted from
differences model 1420. This creates a modified data set (e.g.,
band of data) that can be reliably used to perform statistical
analysis. The modified data set is reliable because regions of high
variabilities (e.g., occlusal surfaces) are omitted from the
statistical analyses. After statistical analysis, regions 1705-1725
each has a standard deviation higher than 20 microns. In FIG. 17,
areas with more intense shading have higher variability than areas
with lighter shading.
FIG. 18 is a perspective view of a modified differences model 1800
having sprue areas 1805 and 1810 being omitted from the original
differences model 1420. A sprue area is a location on the surface
of a tooth where a support material connects bridge 1450 to the
milling block (not shown). Typically, a bridge has three or more
sprues to secure the bridge to the milling block. As previously
mentioned, sprue areas can be highly variable because the sprues
are broken off manually when the bridge is removed from the milling
block. Next, the sprue areas are manually sanded and smoothened for
glazing. Accordingly, process 1400 can remove sprue areas 1805 and
1810 to further increase the reliability of the modified
differences model for statistical analysis.
FIGS. 19A-D are bar charts illustrating distribution of data points
of modified differences models in accordance with some embodiments
of the present disclosure. FIGS. 19A-D also illustrate various
failure modes of a bridge. Referring to FIG. 19A, QC module 215 can
fail the bridge of this example differences model because of low EF
(enlargement factor) as indicated by the shift in distribution in
the negative direction and the two peaks. Additionally, the
standard deviation of this distribution is 40 microns.
In FIG. 19B, this sample fails quality control because the
distribution of this example differences model has two peaks and
has a standard deviation of 39 microns. The second peak is biased
on the positive side--indicating a high EF.
FIG. 19C illustrates a slight shift in the distribution curve
toward the negative direction with as standard deviation of 21
microns. This sample is borderline good but can be rejected due to
the slight shift in the distribution toward the negative and a
slightly high standard deviation.
Similar to FIG. 19C, FIG. 19D represent a distribution with a
negative shift but is more pronounced than FIG. 19C. The standard
deviation of the distribution of FIG. 19D is 23 microns.
Accordingly, QC module 215 can mark this sample as failed because
the standard deviation is higher than 20 microns.
Prosthesis Fixtures
FIG. 20 is a perspective view of a fixture 2000 for securing a
dental prosthesis 2005 during the scanning process, which requires
prosthesis 2005 to be completely secured and stationary while the
scanning takes place. Fixture 2000 employs two vacuum-assisted
holders (hidden from view under prosthesis 2005) to securely hold
prosthesis 2005. Each holder is located at the distal end of
swingable arms 2010 and 2015. Arm 2010 is pivotable about axis
2020, and arm 2015 is pivotable about axis 2025. Both arms 2010 and
2015 can be pivoted inward (toward each other) or outward (away
from each other). The pivotable motion allows the holder at the
distal end of the arm to apply a constant pressure against the
internal cavity (not shown) of prosthesis 2005, which helps keep
prosthesis 2005 stationary.
Fixture 2000 also includes an adaptor (e.g., inlet valve) 2030 to
receive an air hose, which is connected to a motor that generates a
vacuum at the holder at the end of each arm. The vacuum-assisted
holders hold down prosthesis 2005 with air suction and at the same
time applying a constant pressure against the side wall of an
internal cavity of prosthesis 2005.
FIG. 21 illustrates fixture 2000 without prosthesis 2005 mounted on
holders 2105 and 2110. Each of holders 2105 and 2110 includes an
opening (i.e., 2115 and 2120) that extends to inlet valve 2030.
Once the suction pump is activated, a vacuum is created at each of
the openings 2115 and 2120.
As shown, arm 2010 includes a distal portion 2125, and arms 2015
includes a distal portion 2130. Although not shown, fixture 2000
includes internal channels (hidden) that connect inlet valve 2030
to openings 2115 and 2120. One of the internal channels can run
along distal portion 2125 and horizontal proximal portion 2135 of
arm 2010. Similarly, a second internal channel can run along distal
portion 2130 and horizontal proximal portion 2140 of arm 2015.
FIGS. 22A and 22B are perspective views of fixture 2000 with
several components being hidden so that support arm 2200 can
clearly be shown. Support arm 2200 can be raised (see FIG. 22B) so
that it engages the bottom of prosthesis 2005 to provide support if
needed. For certain bridge, the size of the bridge can be long
(e.g., a full upper dental prosthesis) such that support is needed
to provide additional stability. The height of support arm 2200 can
be adjusted and can secured in place using a spring, a stop screw,
friction fitting, etc.
FIG. 23 is a perspective view of another fixture 2300 in accordance
with some embodiments of the present disclosure. Fixture 2300
includes a plurality of push bars 2305, 2310, 2315, 2320, and 2325.
Each push bar is slidably disposed in a slot (e.g., slot 2330) of
fixture 2300, which has a total of seven slots. Each push bar
includes a hole (not shown) in which a support post (e.g., post
2335, 2340) can be placed. The location of support posts 2335 and
2340 depend on the size and width of the dental prosthesis. For a
wide dental prosthesis, post 2335 and 2340 would have to be placed
farther apart. For example, post 2335 can be secured to a hole of
push bar 2305, thereby creating a wider support distance between
posts 2335 and 2340. Dental prosthesis 2350 can be secured into
place using pressure being applied by posts 2335 and 2340. Each
post can have a holder (hidden under dental prosthesis 2350) with a
sticky substance to grip the internal cavity of dental prosthesis
2350. The holder can be sized to have a tight fit within the
internal cavity of dental prosthesis 2350. To securely hold dental
prosthesis 2350 in place, post 2335 can be configured to apply
pressure to the internal cavity wall of dental prosthesis 2350 in a
first direction, and post 2340 can be configured to apply pressure
to the internal cavity wall of dental prosthesis 2350 in a second
(and opposite) direction. In this way, prosthesis 2350 can be
securely held.
FIG. 24 illustrates an overall system or apparatus 2400 in which
modules 210 and 215 and process 1200 can be implemented. In
accordance with various aspects of the disclosure, an element, or
any portion of an element, or any combination of elements may be
implemented with a processing system 2414 that includes one or more
processing circuits 2404. Processing circuits 2404 may include
micro-processing circuits, microcontrollers, digital signal
processing circuits (DSPs), field programmable gate arrays (FPGAs),
programmable logic devices (PLDs), state machines, gated logic,
discrete hardware circuits, and other suitable hardware configured
to perform the various functionality described throughout this
disclosure. That is, the processing circuit 2404 may be used to
implement any one or more of the processes described above and
illustrated in FIGS. 4 through 12.
In the example of FIG. 24, the processing system 2414 may be
implemented with a bus architecture, represented generally by the
bus 2402. The bus 2402 may include any number of interconnecting
buses and bridges depending on the specific application of the
processing system 2414 and the overall design constraints. The bus
2402 links various circuits including one or more processing
circuits (represented generally by the processing circuit 2404),
the storage device 2405, and a machine-readable,
processor-readable, processing circuit-readable or
computer-readable media (represented generally by a non-transitory
machine-readable medium 2406.) The bus 2402 may also link various
other circuits such as timing sources, peripherals, voltage
regulators, and power management circuits, which are well known in
the art, and therefore, will not be described any further. The bus
interface 2408 provides an interface between bus 2402 and a
transceiver 2410. The transceiver 2410 provides a means for
communicating with various other apparatus over a transmission
medium. Depending upon the nature of the apparatus, a user
interface 2412 (e.g., keypad, display, speaker, microphone,
touchscreen, motion sensor) may also be provided.
The processing circuit 2404 is responsible for managing the bus
2402 and for general processing, including the execution of
software stored on the machine-readable medium 2406. The software,
when executed by processing circuit 2404, causes processing system
2414 to perform the various functions described herein for any
particular apparatus. Machine-readable medium 2406 may also be used
for storing data that is manipulated by processing circuit 2404
when executing software.
One or more processing circuits 2404 in the processing system may
execute software or software components. Software shall be
construed broadly to mean instructions, instruction sets, code,
code segments, program code, programs, subprograms, software
modules, applications, software applications, software packages,
routines, subroutines, objects, executables, threads of execution,
procedures, functions, etc., whether referred to as software,
firmware, middleware, microcode, hardware description language, or
otherwise. A processing circuit may perform the tasks. A code
segment may represent a procedure, a function, a subprogram, a
program, a routine, a subroutine, a module, a software package, a
class, or any combination of instructions, data structures, or
program statements. A code segment may be coupled to another code
segment or a hardware circuit by passing and/or receiving
information, data, arguments, parameters, or memory or storage
contents. Information, arguments, parameters, data, etc. may be
passed, forwarded, or transmitted via any suitable means including
memory sharing, message passing, token passing, network
transmission, etc.
The software may reside on machine-readable medium 2406. The
machine-readable medium 2406 may be a non-transitory
machine-readable medium. A non-transitory processing
circuit-readable, machine-readable or computer-readable medium
includes, by way of example, a magnetic storage device (e.g., hard
disk, floppy disk, magnetic strip), an optical disk (e.g., a
compact disc (CD) or a digital versatile disc (DVD)), a smart card,
a flash memory device (e.g., a card, a stick, or a key drive), RAM,
ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an
electrically erasable PROM (EEPROM), a register, a removable disk,
a hard disk, a CD-ROM and any other suitable medium for storing
software and/or instructions that may be accessed and read by a
machine or computer. The terms "machine-readable medium",
"computer-readable medium", "processing circuit-readable medium"
and/or "processor-readable medium" may include, but are not limited
to, non-transitory media such as portable or fixed storage devices,
optical storage devices, and various other media capable of
storing, containing or carrying instruction(s) and/or data. Thus,
the various methods described herein may be fully or partially
implemented by instructions and/or data that may be stored in a
"machine-readable medium," "computer-readable medium," "processing
circuit-readable medium" and/or "processor-readable medium" and
executed by one or more processing circuits, machines and/or
devices. The machine-readable medium may also include, by way of
example, a carrier wave, a transmission line, and any other
suitable medium for transmitting software and/or instructions that
may be accessed and read by a computer.
The machine-readable medium 2406 may reside in the processing
system 2414, external to the processing system 2414, or distributed
across multiple entities including the processing system 2414. The
machine-readable medium 2406 may be embodied in a computer program
product. By way of example, a computer program product may include
a machine-readable medium in packaging materials. Those skilled in
the art will recognize how best to implement the described
functionality presented throughout this disclosure depending on the
particular application and the overall design constraints imposed
on the overall system.
One or more of the components, steps, features, and/or functions
illustrated in the figures may be rearranged and/or combined into a
single component, block, feature or function or embodied in several
components, steps, or functions. Additional elements, components,
steps, and/or functions may also be added without departing from
the disclosure. The apparatus, devices, and/or components
illustrated in the Figures may be configured to perform one or more
of the methods, features, or steps described in the Figures. The
algorithms described herein may also be efficiently implemented in
software and/or embedded in hardware.
Reference in the specification to "one embodiment" or "an
embodiment" means that a particular feature, structure, or
characteristic described in connection with the embodiment is
included in at least one embodiment of the invention. The
appearances of the phrase "in one embodiment" in various places in
the specification are not necessarily all referring to the same
embodiment.
Some portions of the following detailed description are presented
in terms of algorithms and symbolic representations of operations
on data bits within a computer memory. These algorithmic
descriptions and representations are the methods used by those
skilled in the data processing arts to most effectively convey the
substance of their work to others skilled in the art. An algorithm
is here, and generally, conceived to be a self-consistent sequence
of steps leading to a desired result. The steps are those requiring
physical manipulations of physical quantities. Usually, though not
necessarily, these quantities take the form of electrical or
magnetic signals capable of being stored, transferred, combined,
compared or otherwise manipulated. It has proven convenient at
times, principally for reasons of common usage, to refer to these
signals as bits, values, elements, symbols, characters, terms,
numbers or the like.
It should be borne in mind, however, that all of these and similar
terms are to be associated with the appropriate physical quantities
and are merely convenient labels applied to these quantities.
Unless specifically stated otherwise as apparent from the following
disclosure, it is appreciated that throughout the disclosure terms
such as "processing," "computing," "calculating," "determining,"
"displaying" or the like, refer to the action and processes of a
computer system, or similar electronic computing device, that
manipulates and transforms data represented as physical
(electronic) quantities within the computer system's registers and
memories into other data similarly represented as physical
quantities within the computer system's memories or registers or
other such information storage, transmission or display.
Finally, the algorithms and displays presented herein are not
inherently related to any particular computer or other apparatus.
Various general-purpose systems may be used with programs in
accordance with the teachings herein, or it may prove convenient to
construct more specialized apparatus to perform the required method
steps. The required structure for a variety of these systems will
appear from the description below. It will be appreciated that a
variety of programming languages may be used to implement the
teachings of the invention as described herein.
The figures and the following description describe certain
embodiments by way of illustration only. One skilled in the art
will readily recognize from the following description that
alternative embodiments of the structures and methods illustrated
herein may be employed without departing from the principles
described herein. Reference will now be made in detail to several
embodiments, examples of which are illustrated in the accompanying
figures. It is noted that wherever practicable similar or like
reference numbers may be used in the figures to indicate similar or
like functionality.
The foregoing description of the embodiments of the present
invention has been presented for the purposes of illustration and
description. It is not intended to be exhaustive or to limit the
present invention to the precise form disclosed. Many modifications
and variations are possible in light of the above teaching. It is
intended that the scope of the present invention be limited not by
this detailed description, but rather by the claims of this
application. As will be understood by those familiar with the art,
the present invention may be embodied in other specific forms
without departing from the spirit or essential characteristics
thereof. Likewise, the particular naming and division of the
modules, routines, features, attributes, methodologies and other
aspects are not mandatory or significant, and the mechanisms that
implement the present invention or its features may have different
names, divisions and/or formats.
Furthermore, as will be apparent to one of ordinary skill in the
relevant art, the modules, routines, features, attributes,
methodologies and other aspects of the present invention can be
implemented as software, hardware, firmware or any combination of
the three. Also, wherever a component, an example of which is a
module, of the present invention is implemented as software, the
component can be implemented as a standalone program, as part of a
larger program, as a plurality of separate programs, as a
statically or dynamically linked library, as a kernel loadable
module, as a device driver, and/or in every and any other way known
now or in the future to those of ordinary skill in the art of
computer programming.
Additionally, the present invention is in no way limited to
implementation in any specific programming language, or for any
specific operating system or environment. Accordingly, the
disclosure of the present invention is intended to be illustrative,
but not limiting, of the scope of the present invention, which is
set forth in the following claims.
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